Athough GANs were designed as an unsupervised learning strategy, they are proven to be more stable and effective when coupled with labels that are either injected as input conditionings [1]}, [2]} or that help classify some traits about the data being generated [3]}, [4]}. Also the successful speech synthesis model parallel WaveNet [5]} proved that a multi-task aggregation on top of the speech generative model is beneficial to improve speaker identity, prosodic, and content traits.
